The Essay and Multiple Choice Questionnaires Are Not Dead, But No Longer Enough: Generative AI and Assurance of Learning

Abstract

Since the rapid normalisation of generative artificial intelligence (GenAI) in tertiary education, much scholarly and sector discourse has framed the technology in the binary as either a threat to academic integrity or a tool for enhancement. This Editorial argues that such framings obscure a more consequential issue: generative AI has not caused a crisis of assessment validity, but has exposed the limits of long-standing, artefact-based approaches to assurance of learning (AoL). Drawing on contemporary scholarship on post-plagiarism contexts, assurance of learning, academic integrity, and relational pedagogy, this Editorial examines why traditional reliance on essays and multiple-choice questionnaires is no longer sufficient to support defensible judgements about learning when authorship cannot be reliably observed. It argues for a shift from verification-focused assessment practices towards judgement-based, programmatic, and relational approaches that foreground transparency, process, and longitudinal evidence of learning. In doing so, the Editorial seeks to reorient debate within higher education away from detection and control, and towards the design of assessment systems capable of sustaining credible assurance of learning under conditions of epistemic uncertainty.